stochastic gradient descent

SAGA with Arbitrary Sampling ICML 2019 Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy AAAI 2019 Self-similar Epochs: Value in arrangement ICML 2019 Semi-Cyclic Stochastic Gradient Descent ICML 2019 SGD Converges to Global Minimum in Deep Learning via Star-convex Path ICLR 2019 SGD: General Analysis and Improved Rates ICML 2019 SGD on Neural Networks Learns Functions of Increasing Complexity NeurIPS 2019 SGD without Replacement: Sharper Rates for General Smooth Convex Functions ICML 2019 Sharp Analysis for Nonconvex SGD Escaping from Saddle Points COLT 2019 Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning ICCV 2019 SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points NeurIPS 2019 Stagewise Training Accelerates Convergence of Testing Error Over SGD NeurIPS 2019 Stochastic Gradient Descent Learns State Equations with Nonlinear Activations COLT 2019 Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate AISTATS 2019 Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors AISTATS 2019 Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization ICLR 2019 Stochastic Gradient Push for Distributed Deep Learning ICML 2019 Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations JMLR 2019 Stochastic Nonconvex Optimization with Large Minibatches ALT 2019 Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers NeurIPS 2019 Submodular Batch Selection for Training Deep Neural Networks IJCAI 2019 Subspace Inference for Bayesian Deep Learning UAI 2019 Surfing: Iterative Optimization Over Incrementally Trained Deep Networks NeurIPS 2019 Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization ICML 2019 Taming the Noisy Gradient: Train Deep Neural Networks with Small Batch Sizes IJCAI 2019